Fraud Analytics: A Guide to Data Science for Fraud Detection - 20 CPE Hours (ACC590)
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention.
It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak.
- Examine fraud patterns in historical data
- Utilize labeled, unlabeled, and networked data
- Detect fraud before the damage cascades
- Reduce losses, increase recovery, and tighten security
The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.
Program Level: Basic
Advanced Preparation: None
Instructional Method: This program is a QAS Self Study offering.
Field of Study: Fraud
Registration Requirements: In order to purchase this program, you can add this course to your cart and continue with the purchase, or you may contact Sammie McComas at firstname.lastname@example.org or 1-866-412-5352.
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Complaint Resolution Policy: All complaints will be handled in a timely and efficient manner to ensure the complete satisfaction of our students. For more information regarding administrative policies such as complaint and refund, please contact our offices at 1-866-412-5352.